{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experiment: \n",
    "\n",
    "Evaluate pruning by magnitude weighted by coactivations (more thorough evaluation), compare it to baseline (SET), in GSC. Applied only to linear layers\n",
    "\n",
    "#### Motivation.\n",
    "\n",
    "Check if results are consistently above baseline.\n",
    "\n",
    "#### Conclusion\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# experiment configurations\n",
    "base_exp_config = dict(\n",
    "    device=\"cuda\",\n",
    "    # dataset related\n",
    "    dataset_name=\"PreprocessedGSC\",\n",
    "    data_dir=os.path.expanduser(\"~/nta/datasets/gsc\"),\n",
    "    batch_size_train=(4, 16),\n",
    "    batch_size_test=1000,\n",
    "    # network related\n",
    "    network=\"GSCHeb\",\n",
    "    optim_alg=\"SGD\",\n",
    "    momentum=0,\n",
    "    learning_rate=0.01,\n",
    "    weight_decay=0.01,\n",
    "    lr_scheduler=\"MultiStepLR\",\n",
    "    lr_milestones=[30, 60, 90],\n",
    "    lr_gamma=0.90,\n",
    "    use_kwinners=True,\n",
    "    # model related\n",
    "    model=tune.grid_search([\"DSNNWeightedMag\", \"DSNNMixedHeb\", \"SparseModel\"]),\n",
    "    on_perc=tune.grid_search(list(np.arange(0, 0.101, 0.005))),\n",
    "    # sparse related\n",
    "    hebbian_prune_perc=None,\n",
    "    hebbian_grow=False,\n",
    "    weight_prune_perc=0.3,\n",
    "    pruning_early_stop=2,\n",
    "    # additional validation\n",
    "    test_noise=False,\n",
    "    # debugging\n",
    "    debug_weights=True,\n",
    "    debug_sparse=True,\n",
    ")\n",
    "\n",
    "# ray configurations\n",
    "tune_config = dict(\n",
    "    name=__file__.replace(\".py\", \"\") + \"_eval3\",\n",
    "    num_samples=1,\n",
    "    # num_samples=3,\n",
    "    local_dir=os.path.expanduser(\"~/nta/results\"),\n",
    "    checkpoint_freq=0,\n",
    "    checkpoint_at_end=False,\n",
    "    stop={\"training_iteration\": 100},\n",
    "    resources_per_trial={\"cpu\": 1, \"gpu\": .25},\n",
    "    loggers=DEFAULT_LOGGERS,\n",
    "    verbose=0,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import tabulate\n",
    "import pprint\n",
    "import click\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ray.tune.commands import *\n",
    "from nupic.research.frameworks.dynamic_sparse.common.browser import *\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.set_palette(\"colorblind\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and check data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "improved_mag_gsc_eval3\n",
      "improved_mag_gsc_eval4\n"
     ]
    }
   ],
   "source": [
    "exps = ['improved_mag_gsc_eval3', 'improved_mag_gsc_eval4']\n",
    "paths = [os.path.expanduser(\"~/nta/results/{}\".format(e)) for e in exps]\n",
    "df = load_many(paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experiment Name</th>\n",
       "      <th>train_acc_max</th>\n",
       "      <th>train_acc_max_epoch</th>\n",
       "      <th>train_acc_min</th>\n",
       "      <th>train_acc_min_epoch</th>\n",
       "      <th>train_acc_median</th>\n",
       "      <th>train_acc_last</th>\n",
       "      <th>val_acc_max</th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th>val_acc_min</th>\n",
       "      <th>...</th>\n",
       "      <th>model</th>\n",
       "      <th>momentum</th>\n",
       "      <th>network</th>\n",
       "      <th>on_perc</th>\n",
       "      <th>optim_alg</th>\n",
       "      <th>pruning_early_stop</th>\n",
       "      <th>test_noise</th>\n",
       "      <th>use_kwinners</th>\n",
       "      <th>weight_decay</th>\n",
       "      <th>weight_prune_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_model=DSNNWeightedMag,on_perc=0.0</td>\n",
       "      <td>0.092960</td>\n",
       "      <td>19</td>\n",
       "      <td>0.087052</td>\n",
       "      <td>47</td>\n",
       "      <td>0.089689</td>\n",
       "      <td>0.089542</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>22</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>DSNNWeightedMag</td>\n",
       "      <td>0</td>\n",
       "      <td>GSCHeb</td>\n",
       "      <td>0.000</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1_model=DSNNMixedHeb,on_perc=0.0</td>\n",
       "      <td>0.091934</td>\n",
       "      <td>24</td>\n",
       "      <td>0.085880</td>\n",
       "      <td>33</td>\n",
       "      <td>0.089591</td>\n",
       "      <td>0.089542</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>DSNNMixedHeb</td>\n",
       "      <td>0</td>\n",
       "      <td>GSCHeb</td>\n",
       "      <td>0.000</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2_model=SparseModel,on_perc=0.0</td>\n",
       "      <td>0.092325</td>\n",
       "      <td>38</td>\n",
       "      <td>0.085587</td>\n",
       "      <td>99</td>\n",
       "      <td>0.089322</td>\n",
       "      <td>0.085587</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>SparseModel</td>\n",
       "      <td>0</td>\n",
       "      <td>GSCHeb</td>\n",
       "      <td>0.000</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3_model=DSNNWeightedMag,on_perc=0.005</td>\n",
       "      <td>0.399522</td>\n",
       "      <td>7</td>\n",
       "      <td>0.156821</td>\n",
       "      <td>0</td>\n",
       "      <td>0.199736</td>\n",
       "      <td>0.196026</td>\n",
       "      <td>0.394592</td>\n",
       "      <td>8</td>\n",
       "      <td>0.145376</td>\n",
       "      <td>...</td>\n",
       "      <td>DSNNWeightedMag</td>\n",
       "      <td>0</td>\n",
       "      <td>GSCHeb</td>\n",
       "      <td>0.005</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4_model=DSNNMixedHeb,on_perc=0.005</td>\n",
       "      <td>0.365931</td>\n",
       "      <td>11</td>\n",
       "      <td>0.181330</td>\n",
       "      <td>0</td>\n",
       "      <td>0.296016</td>\n",
       "      <td>0.296065</td>\n",
       "      <td>0.362461</td>\n",
       "      <td>11</td>\n",
       "      <td>0.087382</td>\n",
       "      <td>...</td>\n",
       "      <td>DSNNMixedHeb</td>\n",
       "      <td>0</td>\n",
       "      <td>GSCHeb</td>\n",
       "      <td>0.005</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Experiment Name  train_acc_max  train_acc_max_epoch  \\\n",
       "0    0_model=DSNNWeightedMag,on_perc=0.0       0.092960                   19   \n",
       "1       1_model=DSNNMixedHeb,on_perc=0.0       0.091934                   24   \n",
       "2        2_model=SparseModel,on_perc=0.0       0.092325                   38   \n",
       "3  3_model=DSNNWeightedMag,on_perc=0.005       0.399522                    7   \n",
       "4     4_model=DSNNMixedHeb,on_perc=0.005       0.365931                   11   \n",
       "\n",
       "   train_acc_min  train_acc_min_epoch  train_acc_median  train_acc_last  \\\n",
       "0       0.087052                   47          0.089689        0.089542   \n",
       "1       0.085880                   33          0.089591        0.089542   \n",
       "2       0.085587                   99          0.089322        0.085587   \n",
       "3       0.156821                    0          0.199736        0.196026   \n",
       "4       0.181330                    0          0.296016        0.296065   \n",
       "\n",
       "   val_acc_max  val_acc_max_epoch  val_acc_min  ...            model  \\\n",
       "0     0.103448                 22     0.000000  ...  DSNNWeightedMag   \n",
       "1     0.103448                 10     0.000000  ...     DSNNMixedHeb   \n",
       "2     0.103448                  5     0.000000  ...      SparseModel   \n",
       "3     0.394592                  8     0.145376  ...  DSNNWeightedMag   \n",
       "4     0.362461                 11     0.087382  ...     DSNNMixedHeb   \n",
       "\n",
       "   momentum  network on_perc  optim_alg pruning_early_stop test_noise  \\\n",
       "0         0   GSCHeb   0.000        SGD                  2      False   \n",
       "1         0   GSCHeb   0.000        SGD                  2      False   \n",
       "2         0   GSCHeb   0.000        SGD                  2      False   \n",
       "3         0   GSCHeb   0.005        SGD                  2      False   \n",
       "4         0   GSCHeb   0.005        SGD                  2      False   \n",
       "\n",
       "   use_kwinners  weight_decay weight_prune_perc  \n",
       "0          True          0.01               0.3  \n",
       "1          True          0.01               0.3  \n",
       "2          True          0.01               0.3  \n",
       "3          True          0.01               0.3  \n",
       "4          True          0.01               0.3  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace hebbian prine\n",
    "df['hebbian_prune_perc'] = df['hebbian_prune_perc'].replace(np.nan, 0.0, regex=True)\n",
    "df['weight_prune_perc'] = df['weight_prune_perc'].replace(np.nan, 0.0, regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Experiment Name', 'train_acc_max', 'train_acc_max_epoch',\n",
       "       'train_acc_min', 'train_acc_min_epoch', 'train_acc_median',\n",
       "       'train_acc_last', 'val_acc_max', 'val_acc_max_epoch', 'val_acc_min',\n",
       "       'val_acc_min_epoch', 'val_acc_median', 'val_acc_last', 'val_acc_all',\n",
       "       'epochs', 'experiment_file_name', 'experiment_base_path', 'trial_time',\n",
       "       'mean_epoch_time', 'scatter_plot_dicts', 'batch_size_test',\n",
       "       'batch_size_train', 'data_dir', 'dataset_name', 'debug_sparse',\n",
       "       'debug_weights', 'device', 'hebbian_grow', 'hebbian_prune_perc',\n",
       "       'learning_rate', 'lr_gamma', 'lr_milestones', 'lr_scheduler', 'model',\n",
       "       'momentum', 'network', 'on_perc', 'optim_alg', 'pruning_early_stop',\n",
       "       'test_noise', 'use_kwinners', 'weight_decay', 'weight_prune_perc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(269, 43)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Experiment Name                          1_model=DSNNMixedHeb,on_perc=0.0\n",
       "train_acc_max                                                   0.0919344\n",
       "train_acc_max_epoch                                                    24\n",
       "train_acc_min                                                   0.0858803\n",
       "train_acc_min_epoch                                                    33\n",
       "train_acc_median                                                0.0895909\n",
       "train_acc_last                                                   0.089542\n",
       "val_acc_max                                                      0.103448\n",
       "val_acc_max_epoch                                                      10\n",
       "val_acc_min                                                             0\n",
       "val_acc_min_epoch                                                       0\n",
       "val_acc_median                                                  0.0971787\n",
       "val_acc_last                                                    0.0971787\n",
       "val_acc_all             0     0.000000\n",
       "1     0.101489\n",
       "2     0.097179\n",
       "3...\n",
       "epochs                                                                100\n",
       "experiment_file_name    /Users/mcaporale/nta/results/improved_mag_gsc_...\n",
       "experiment_base_path                               improved_mag_gsc_eval3\n",
       "trial_time                                                        26.1431\n",
       "mean_epoch_time                                                  0.261431\n",
       "scatter_plot_dicts                                                     {}\n",
       "batch_size_test                                                      1000\n",
       "batch_size_train                                                       10\n",
       "data_dir                                    /home/ubuntu/nta/datasets/gsc\n",
       "dataset_name                                              PreprocessedGSC\n",
       "debug_sparse                                                         True\n",
       "debug_weights                                                        True\n",
       "device                                                               cuda\n",
       "hebbian_grow                                                        False\n",
       "hebbian_prune_perc                                                      0\n",
       "learning_rate                                                        0.01\n",
       "lr_gamma                                                              0.9\n",
       "lr_milestones                                                          60\n",
       "lr_scheduler                                                  MultiStepLR\n",
       "model                                                        DSNNMixedHeb\n",
       "momentum                                                                0\n",
       "network                                                            GSCHeb\n",
       "on_perc                                                                 0\n",
       "optim_alg                                                             SGD\n",
       "pruning_early_stop                                                      2\n",
       "test_noise                                                          False\n",
       "use_kwinners                                                         True\n",
       "weight_decay                                                         0.01\n",
       "weight_prune_perc                                                     0.3\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "DSNNMixedHeb       90\n",
       "DSNNWeightedMag    90\n",
       "SparseModel        89\n",
       "Name: model, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('model')['model'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiment Details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Did any  trials failed?\n",
    "df[df[\"epochs\"]<30][\"epochs\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(264, 43)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing failed or incomplete trials\n",
    "df_origin = df.copy()\n",
    "df = df_origin[df_origin[\"epochs\"]>=30]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "264    26\n",
       "265    23\n",
       "266    26\n",
       "267    11\n",
       "268     1\n",
       "Name: epochs, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which ones failed?\n",
    "# failed, or still ongoing?\n",
    "df_origin['failed'] = df_origin[\"epochs\"]<30\n",
    "df_origin[df_origin['failed']]['epochs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper functions\n",
    "def mean_and_std(s):\n",
    "    return \"{:.3f} ± {:.3f}\".format(s.mean(), s.std())\n",
    "\n",
    "def round_mean(s):\n",
    "    return \"{:.0f}\".format(round(s.mean()))\n",
    "\n",
    "stats = ['min', 'max', 'mean', 'std']\n",
    "\n",
    "def agg(columns, filter=None, round=3):\n",
    "    if filter is None:\n",
    "        return (df.groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n",
    "    else:\n",
    "        return (df[filter].groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gsc-smalldense\n"
     ]
    }
   ],
   "source": [
    "def agg2(columns, filter=None, round=3):\n",
    "    if filter is None:\n",
    "        return (df2.groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n",
    "    else:\n",
    "        return (df2[filter].groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n",
    "\n",
    "base = os.path.join('gsc-smalldense-2019-10-11-exp1')\n",
    "exps = [\n",
    "    os.path.join(base, exp) for exp in [\n",
    "        'gsc-smalldense'\n",
    "    ]\n",
    "]\n",
    "paths = [os.path.expanduser(\"~/nta/results/{}\".format(e)) for e in exps]\n",
    "df2 = load_many(paths)\n",
    "\n",
    "df_agg = agg2(['equivalent_on_perc'])\n",
    "df_agg\n",
    "\n",
    "equivalent_on_percs = df_agg.index.values\n",
    "\n",
    "val_means = df_agg['val_acc_max']['mean']\n",
    "val_means = np.array(val_means)\n",
    "val_stds = df_agg['val_acc_max']['std']\n",
    "val_stds = np.array(val_stds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Does improved weight pruning outperforms regular SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>round_mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>54</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.228</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>63</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.223</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>74</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.808</td>\n",
       "      <td>0.238</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                val_acc_max_epoch val_acc_max                      model\n",
       "                       round_mean         min    max   mean    std count\n",
       "model                                                                   \n",
       "DSNNMixedHeb                   54       0.103  0.954  0.823  0.228    88\n",
       "DSNNWeightedMag                63       0.103  0.952  0.834  0.223    88\n",
       "SparseModel                    74       0.103  0.949  0.808  0.238    88"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg(['model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>round_mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>on_perc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000</th>\n",
       "      <td>10</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.001</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.005</th>\n",
       "      <td>25</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.059</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.010</th>\n",
       "      <td>35</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.044</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.015</th>\n",
       "      <td>40</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.028</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.020</th>\n",
       "      <td>50</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.025</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.025</th>\n",
       "      <td>56</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.021</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.030</th>\n",
       "      <td>73</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.016</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.035</th>\n",
       "      <td>69</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.015</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.040</th>\n",
       "      <td>70</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.010</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.045</th>\n",
       "      <td>77</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.009</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.050</th>\n",
       "      <td>78</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.006</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.055</th>\n",
       "      <td>73</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.004</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.060</th>\n",
       "      <td>73</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.005</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.065</th>\n",
       "      <td>82</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.006</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.070</th>\n",
       "      <td>74</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.006</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.075</th>\n",
       "      <td>84</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.003</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.080</th>\n",
       "      <td>80</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.004</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.085</th>\n",
       "      <td>82</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.004</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.090</th>\n",
       "      <td>83</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.002</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.095</th>\n",
       "      <td>74</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.003</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.100</th>\n",
       "      <td>83</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.002</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        val_acc_max_epoch val_acc_max                      model\n",
       "               round_mean         min    max   mean    std count\n",
       "on_perc                                                         \n",
       "0.000                  10       0.103  0.106  0.104  0.001    15\n",
       "0.005                  25       0.218  0.425  0.352  0.059    15\n",
       "0.010                  35       0.609  0.732  0.666  0.044    15\n",
       "0.015                  40       0.735  0.822  0.779  0.028    15\n",
       "0.020                  50       0.807  0.880  0.836  0.025    12\n",
       "0.025                  56       0.841  0.905  0.875  0.021    12\n",
       "0.030                  73       0.865  0.915  0.891  0.016    12\n",
       "0.035                  69       0.878  0.922  0.900  0.015    12\n",
       "0.040                  70       0.895  0.927  0.914  0.010    12\n",
       "0.045                  77       0.910  0.942  0.926  0.009    12\n",
       "0.050                  78       0.915  0.938  0.927  0.006    12\n",
       "0.055                  73       0.928  0.942  0.933  0.004    12\n",
       "0.060                  73       0.931  0.947  0.936  0.005    12\n",
       "0.065                  82       0.929  0.947  0.940  0.006    12\n",
       "0.070                  74       0.924  0.947  0.938  0.006    12\n",
       "0.075                  84       0.939  0.949  0.944  0.003    12\n",
       "0.080                  80       0.938  0.952  0.944  0.004    12\n",
       "0.085                  82       0.938  0.951  0.945  0.004    12\n",
       "0.090                  83       0.943  0.951  0.948  0.002    12\n",
       "0.095                  74       0.942  0.954  0.947  0.003    12\n",
       "0.100                  83       0.946  0.954  0.949  0.002    12"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg(['on_perc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>round_mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>on_perc</th>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.04</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>46</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>84</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>81</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        val_acc_max_epoch val_acc_max                       \\\n",
       "                               round_mean         min    max   mean    std   \n",
       "on_perc model                                                                \n",
       "0.04    DSNNMixedHeb                   46       0.911  0.916  0.914  0.002   \n",
       "        DSNNWeightedMag                84       0.922  0.927  0.924  0.002   \n",
       "        SparseModel                    81       0.895  0.909  0.902  0.006   \n",
       "\n",
       "                        model  \n",
       "                        count  \n",
       "on_perc model                  \n",
       "0.04    DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg(['on_perc', 'model'], df['on_perc'] == 0.04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# translate model names\n",
    "rcParams['figure.figsize'] = 16, 8\n",
    "d = {\n",
    "    'DSNNWeightedMag': 'Dynamic Sparse Neural Network (DSNN)',\n",
    "    'DSNNMixedHeb': 'Sparse Evolutionary Training (SET)',\n",
    "    'SparseModel': 'Static Sparse',        \n",
    "}\n",
    "df_plot = df.copy()\n",
    "df_plot['model'] = df_plot['model'].apply(lambda x: d[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12993f828>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 488,
       "width": 953
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.scatterplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')\n",
    "sns.lineplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>round_mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>on_perc</th>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.000</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>6</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>13</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>12</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.001</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.005</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>10</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.017</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>8</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.014</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>59</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.044</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.010</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>11</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.027</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>15</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.031</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>80</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.623</td>\n",
       "      <td>0.014</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.015</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>18</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.023</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>25</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.011</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>78</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.009</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.020</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>39</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>42</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0.025</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>70</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.007</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.025</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>34</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.008</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>55</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>78</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.011</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.030</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>51</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.011</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>77</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.007</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>90</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.011</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.035</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>65</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.009</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>74</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.009</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>68</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.012</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.040</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>46</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>84</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>81</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.045</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>70</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>81</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.007</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>80</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.055</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>74</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>70</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.003</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>77</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.003</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.060</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>73</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>71</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.004</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>74</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.001</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.065</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>85</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.001</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>80</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.003</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>82</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.070</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>76</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.004</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>82</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.003</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>63</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.075</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>73</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>95</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.001</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>83</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.080</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>73</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.004</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>82</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>86</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.085</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>72</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>88</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.004</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>86</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.003</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.090</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>77</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>87</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.001</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>86</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.003</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.095</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>70</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.004</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>71</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>80</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.003</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.100</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>82</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.003</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>93</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>74</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.001</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        val_acc_max_epoch val_acc_max                       \\\n",
       "                               round_mean         min    max   mean    std   \n",
       "on_perc model                                                                \n",
       "0.000   DSNNMixedHeb                    6       0.103  0.103  0.103  0.000   \n",
       "        DSNNWeightedMag                13       0.103  0.103  0.103  0.000   \n",
       "        SparseModel                    12       0.103  0.106  0.104  0.001   \n",
       "0.005   DSNNMixedHeb                   10       0.351  0.396  0.366  0.017   \n",
       "        DSNNWeightedMag                 8       0.394  0.425  0.406  0.014   \n",
       "        SparseModel                    59       0.218  0.334  0.283  0.044   \n",
       "0.010   DSNNMixedHeb                   11       0.639  0.699  0.666  0.027   \n",
       "        DSNNWeightedMag                15       0.656  0.732  0.710  0.031   \n",
       "        SparseModel                    80       0.609  0.640  0.623  0.014   \n",
       "0.015   DSNNMixedHeb                   18       0.735  0.789  0.776  0.023   \n",
       "        DSNNWeightedMag                25       0.793  0.822  0.809  0.011   \n",
       "        SparseModel                    78       0.745  0.765  0.754  0.009   \n",
       "0.020   DSNNMixedHeb                   39       0.819  0.852  0.836  0.015   \n",
       "        DSNNWeightedMag                42       0.830  0.880  0.859  0.025   \n",
       "        SparseModel                    70       0.807  0.820  0.814  0.007   \n",
       "0.025   DSNNMixedHeb                   34       0.860  0.877  0.872  0.008   \n",
       "        DSNNWeightedMag                55       0.892  0.905  0.900  0.006   \n",
       "        SparseModel                    78       0.841  0.864  0.854  0.011   \n",
       "0.030   DSNNMixedHeb                   51       0.870  0.894  0.886  0.011   \n",
       "        DSNNWeightedMag                77       0.901  0.915  0.908  0.007   \n",
       "        SparseModel                    90       0.865  0.891  0.880  0.011   \n",
       "0.035   DSNNMixedHeb                   65       0.891  0.912  0.900  0.009   \n",
       "        DSNNWeightedMag                74       0.900  0.922  0.913  0.009   \n",
       "        SparseModel                    68       0.878  0.903  0.886  0.012   \n",
       "0.040   DSNNMixedHeb                   46       0.911  0.916  0.914  0.002   \n",
       "        DSNNWeightedMag                84       0.922  0.927  0.924  0.002   \n",
       "        SparseModel                    81       0.895  0.909  0.902  0.006   \n",
       "0.045   DSNNMixedHeb                   70       0.927  0.930  0.929  0.002   \n",
       "        DSNNWeightedMag                81       0.925  0.942  0.934  0.007   \n",
       "        SparseModel                    80       0.910  0.921  0.917  0.005   \n",
       "...                                   ...         ...    ...    ...    ...   \n",
       "0.055   DSNNMixedHeb                   74       0.930  0.942  0.936  0.005   \n",
       "        DSNNWeightedMag                70       0.929  0.936  0.933  0.003   \n",
       "        SparseModel                    77       0.928  0.933  0.930  0.003   \n",
       "0.060   DSNNMixedHeb                   73       0.933  0.947  0.939  0.006   \n",
       "        DSNNWeightedMag                71       0.933  0.942  0.937  0.004   \n",
       "        SparseModel                    74       0.931  0.934  0.933  0.001   \n",
       "0.065   DSNNMixedHeb                   85       0.942  0.945  0.944  0.001   \n",
       "        DSNNWeightedMag                80       0.941  0.947  0.943  0.003   \n",
       "        SparseModel                    82       0.929  0.941  0.934  0.005   \n",
       "0.070   DSNNMixedHeb                   76       0.936  0.945  0.940  0.004   \n",
       "        DSNNWeightedMag                82       0.940  0.947  0.943  0.003   \n",
       "        SparseModel                    63       0.924  0.935  0.932  0.006   \n",
       "0.075   DSNNMixedHeb                   73       0.939  0.949  0.943  0.005   \n",
       "        DSNNWeightedMag                95       0.945  0.948  0.947  0.001   \n",
       "        SparseModel                    83       0.940  0.944  0.943  0.002   \n",
       "0.080   DSNNMixedHeb                   73       0.943  0.952  0.946  0.004   \n",
       "        DSNNWeightedMag                82       0.944  0.948  0.947  0.002   \n",
       "        SparseModel                    86       0.938  0.942  0.940  0.002   \n",
       "0.085   DSNNMixedHeb                   72       0.947  0.951  0.948  0.002   \n",
       "        DSNNWeightedMag                88       0.940  0.949  0.945  0.004   \n",
       "        SparseModel                    86       0.938  0.946  0.943  0.003   \n",
       "0.090   DSNNMixedHeb                   77       0.947  0.951  0.949  0.002   \n",
       "        DSNNWeightedMag                87       0.947  0.949  0.949  0.001   \n",
       "        SparseModel                    86       0.943  0.949  0.945  0.003   \n",
       "0.095   DSNNMixedHeb                   70       0.946  0.954  0.948  0.004   \n",
       "        DSNNWeightedMag                71       0.945  0.950  0.948  0.002   \n",
       "        SparseModel                    80       0.942  0.948  0.944  0.003   \n",
       "0.100   DSNNMixedHeb                   82       0.946  0.954  0.950  0.003   \n",
       "        DSNNWeightedMag                93       0.949  0.952  0.950  0.002   \n",
       "        SparseModel                    74       0.946  0.949  0.948  0.001   \n",
       "\n",
       "                        model  \n",
       "                        count  \n",
       "on_perc model                  \n",
       "0.000   DSNNMixedHeb        5  \n",
       "        DSNNWeightedMag     5  \n",
       "        SparseModel         5  \n",
       "0.005   DSNNMixedHeb        5  \n",
       "        DSNNWeightedMag     5  \n",
       "        SparseModel         5  \n",
       "0.010   DSNNMixedHeb        5  \n",
       "        DSNNWeightedMag     5  \n",
       "        SparseModel         5  \n",
       "0.015   DSNNMixedHeb        5  \n",
       "        DSNNWeightedMag     5  \n",
       "        SparseModel         5  \n",
       "0.020   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.025   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.030   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.035   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.040   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.045   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "...                       ...  \n",
       "0.055   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.060   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.065   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.070   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.075   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.080   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.085   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.090   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.095   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "0.100   DSNNMixedHeb        4  \n",
       "        DSNNWeightedMag     4  \n",
       "        SparseModel         4  \n",
       "\n",
       "[63 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fltr = df_plot['model'] != 'Sparse'\n",
    "agg(['on_perc', 'model'], filter=fltr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "hi\n"
     ]
    }
   ],
   "source": [
    "!source ~/.bash_profile\n",
    "!echo $GOOGLE_DRIVE_PATH\n",
    "!echo hi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 383,
       "width": 615
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_for_paper():\n",
    "    rcParams['figure.figsize'] = 10,6\n",
    "    matplotlib.rc('xtick', labelsize=14)\n",
    "    matplotlib.rc('ytick', labelsize=14)\n",
    "    matplotlib.rc('ytick', labelsize=14)\n",
    "    plt.rcParams.update({'text.usetex': True})\n",
    "    plt.rcParams.update({'axes.labelsize': 14})\n",
    "    plt.rcParams.update({'legend.fontsize': 14, 'legend.loc': 'lower right'})\n",
    "    plt.rcParams.update({'font.size': 14})\n",
    "    plt.rcParams.update({\"axes.grid\": True, \"grid.linewidth\": 0.5})\n",
    "    plt.rcParams.update({'font.family': 'serif'})\n",
    "    plt.rcParams.update({'text.usetex': True})    \n",
    "    \n",
    "    \n",
    "plot_for_paper()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.lineplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')\n",
    "plt.errorbar(\n",
    "    x=equivalent_on_percs,\n",
    "    y=val_means,\n",
    "    yerr=val_stds,\n",
    "    color='k',\n",
    "    marker='.',\n",
    "    lw=0,\n",
    "    elinewidth=2,\n",
    "    capsize=4,\n",
    "    markersize=5,\n",
    "    label=\"Small-Dense Equivalents\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"% of active weights ($\\sigma$)\")\n",
    "plt.ylabel(\"test accuracy\")\n",
    "plt.ylim((0.4, 1.0))\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles=handles[1:], labels=labels[1:])\n",
    "fig_path = r\"~/Google Drive/papers/dynamic_sparse/figures/neurips_2019/active_weights.png\"\n",
    "fig_path = os.path.expanduser(fig_path)\n",
    "\n",
    "plt.savefig(fig_path, dpi=1000, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    # plt.rcParams['figure.figsize'] = (8, 3)\n",
    "    font_size=12\n",
    "#     plt.rcParams.update({'font.size': font_size})\n",
    "#     plt.rcParams.update({'font.family': 'Times New Roman'})\n",
    "#     plt.rcParams.update({'axes.labelsize': font_size})\n",
    "#     plt.rcParams.update({'axes.titlesize': 1.5*font_size})\n",
    "#     plt.rcParams.update({'legend.fontsize': font_size})\n",
    "#     plt.rcParams.update({'xtick.labelsize': font_size})\n",
    "#     plt.rcParams.update({'ytick.labelsize': font_size})\n",
    "#     plt.rcParams.update({'savefig.dpi': 2*plt.rcParams['savefig.dpi']})\n",
    "#     plt.rcParams.update({'xtick.major.size': 3})\n",
    "#     plt.rcParams.update({'xtick.minor.size': 3})\n",
    "#     plt.rcParams.update({'xtick.major.width': 1})\n",
    "#     plt.rcParams.update({'xtick.minor.width': 1})\n",
    "#     plt.rcParams.update({'ytick.major.size': 3})\n",
    "#     plt.rcParams.update({'ytick.minor.size': 3})\n",
    "#     plt.rcParams.update({'ytick.major.width': 1})\n",
    "#     plt.rcParams.update({'ytick.minor.width': 1 })\n",
    "    # plt.rcParams.update({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.scatterplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')\n",
    "sns.lineplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')\n",
    "plt.ylim(0.7,0.98)\n",
    "plt.errorbar(\n",
    "    x=equivalent_on_percs,\n",
    "    y=val_means,\n",
    "    yerr=val_stds,\n",
    "    color='k',\n",
    "    marker='*',\n",
    "    lw=0,\n",
    "    elinewidth=2,\n",
    "    capsize=2,\n",
    "    markersize=10,\n",
    "    label=\"Small-Dense Equivalents\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcParams['figure.figsize'] = 16, 8\n",
    "filter = df_plot['model'] != 'Static'\n",
    "sns.lineplot(data=df_plot[filter], x='on_perc', y='val_acc_max_epoch', hue='model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lineplot(data=df_plot, x='on_perc', y='val_acc_last', hue='model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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